97 results about "Sentence segmentation" patented technology

The invention belongs to the technical field of power equipment operation inspection auxiliary decision making, and particularly relates to a power operation inspection knowledge service method and device, equipment and a medium. The method comprises the following steps: analyzing semantics of query input of a user by utilizing a large semantic model, and determining a corresponding target sub-knowledge base in combination with an intention classification sample library and an intention set; performing sentence segmentation on the document of the target sub-knowledge base, generating document fragments containing continuous sentences through context supplementation, and enabling semantics of the document fragments to be overlapped; calculating a keyword correlation score of the query word and the document fragment; generating a text vector through a semantic coding model, and calculating a semantic correlation score of the query input and the document fragments; fusing the keyword correlation score and the semantic correlation score, and outputting each related document fragment according to a comprehensive score sequence; and inputting query input of a user and related document fragments into the large semantic model, and generating a structured answer in combination with the logic in the operation and inspection field of the power equipment. And the electric power operation and maintenance knowledge service with high accuracy and strong generalization is realized.

The invention provides a tunnel risk reasoning method fusing a knowledge graph and a large language model, which comprises the following steps of: obtaining structured monitoring data and unstructured text data, adopting methods such as field standardization for the structured monitoring data to realize a unified format, adopting methods such as sentence segmentation and word segmentation for the unstructured text data to realize the unified format, and obtaining the structured monitoring data and the unstructured text data; the method comprises the following steps of: extracting entities from data by utilizing a model, extracting a relationship between the entities based on the entities, forming basic triads, forming a sub-graph by the basic triads, integrating to form a knowledge graph, generating a natural language, extracting the sub-graph related to the natural language from the knowledge graph, and converting the sub-graph into a sub-graph in a vector form by utilizing a graph embedding algorithm. The entities and the relation paths of the entities serve as explicit reasoning clues, the natural language, the sub-maps in the vector form and the explicit reasoning clues are input into a large language model, natural language output is generated, multi-source data information is integrated, and high-precision and interpretable tunnel risk early warning is output through the large language model.

The invention provides a medical record information extraction and analysis method and system based on a deep learning large model, and the method comprises the steps: carrying out the semantic understanding of a medical record text through a deep learning model, and recognizing key clinical features; extracting the key clinical features, and carrying out vectorization processing on the extracted key clinical features; performing sentence segmentation and word segmentation on the case text based on a rule engine and the key clinical features; adjusting the segmented words based on a symptom classification rule; and carrying out word frequency statistics on the adjusted segmented words. According to the method, accurate recognition and structured conversion of key clinical features can be ensured through a semantic understanding technology, knowledge graph construction and machine learning model optimization are supported, and a new view angle and a new tool are provided for exploration and research of diseases.

The invention discloses a method and system for jointly extracting entity and relation information in a text, and the method comprises the steps: inputting a text sentence, segmenting the sentence into lexical elements, and obtaining a lexical element sequence; inputting the lexical element sequence into an ALBERT model to generate a first output vector; inputting the lexical element sequence into a TKGTransE knowledge graph embedding model to generate a second output vector; inputting the first output vector and the second output vector into a GFN gating fusion network to generate a first vector representation; and inputting the first vector representation and the lexical element sequence into a non-autoregression decoder to generate a prediction triple, and evaluating a prediction result by adopting a bipartite matching loss function to generate a prediction vector. According to the method, knowledge fusion is realized, and the processing capability of the model on a complex text structure is improved.

A public opinion analysis visualization platform construction method based on sentiment analysis belongs to the technical field of natural language processing, and specifically comprises the following steps: 1, collecting related corpora and data required by sentiment analysis; 2, preprocessing comment data required by sentiment analysis; and filtering invalid texts, and performing sentence segmentation processing. 3, firstly performing text classification on the processed data, then performing sentiment analysis, judging sentiment tendency in a comment text, and finally performing visual display; and 4, performing public opinion early warning based on an obtained sentiment analysis result and a popularity data analysis result. And 5, generating an independent public opinion evaluation report and a summary report through a related API interface of the local large language model. Through the method, the public opinion response efficiency can be remarkably improved, the risk identification capability is enhanced, the complex language understanding bottleneck is broken through, and meanwhile the manual analysis cost is reduced.

The invention provides a false news detection method and device and terminal equipment, and is suitable for the technical field of data processing.The method comprises the steps that statement segmentation and invalid statement filtering processing are conducted on news text information to be detected, and multiple pieces of news statement information to be detected are obtained; generating multiple pieces of to-be-detected news text structured information according to the to-be-detected news text information, the multiple pieces of to-be-detected news statement information, preset to-be-detected news text extraction guide information and a preset to-be-detected news text structured information extraction model; and generating multiple pieces of news text detection information according to the to-be-detected news text information, the multiple pieces of to-be-detected news text structured information and a preset news text detection model. According to the method, the requirements of scenes such as manual review and judicial evidence collection on interpretability and traceability are met, and the accuracy, reliability and engineering reproducibility of false news detection in a complex context are remarkably improved.

The invention discloses an ancient Chinese automatic sentence segmentation and semantic analysis system based on deep learning, and relates to the technical field of ancient Chinese analysis, according to the ancient Chinese automatic sentence segmentation and semantic analysis system based on deep learning, a sentence segmentation module is fused with a gated loop unit (GRU) and a convolutional neural network (CNN), and image font features are combined, so that a sentence segmentation result is obtained; the problems of semantic ambiguity, long-distance dependence, common and false characters and abnormal characters can be effectively solved; a knowledge graph containing historical and cultural common knowledge and vocabulary dynamic relations is constructed through a semantic analysis module, prior knowledge is injected through a graph neural network, and the semantic understanding depth is improved; a sentence segmentation result containing a sentence segmentation basis and a visual semantic analysis result are provided through an output module, so that a user can clearly master processing logic, and credibility and convenience are improved; a large number of unlabeled ancient Chinese texts are efficiently processed by the system, and values of various cultural heritage such as classics and inscriptions are fully mined; the development of grammar and vocabulary research is promoted, and ancient Chinese culture inheritance and propagation are assisted.

The invention relates to the field of intelligent interaction, and provides a production command case-oriented interaction deduction method and system, and the method employs a text analysis and processing technology based on a deep learning model to carry out the sentence segmentation and semantic coding of the text description of a production command case, carries out the semantic coding of the text description of a current trigger event, and carries out the semantic coding of the text description of the current trigger event. In this way, the auxiliary prompt content is automatically generated according to the query matching representation between the text semantic information of the current trigger event and the sentence granularity description semantics of each production command case. Therefore, the production command case fragment most related to the current event can be identified more accurately, and more accurate and personalized auxiliary prompts are dynamically generated according to the current condition, so that power grid production command training and emergency drilling are better supported.

This application discloses an intelligent method and apparatus for detecting active speech with sentence segmentation based on multi-state temporal modeling. The method includes: receiving audio signals from at least one channel; extracting acoustic feature sequences from the audio signals using a target speech recognition model corresponding to the number of channels; determining the probability distribution of each speech frame corresponding to the acoustic feature sequences belonging to different speech activity states, obtaining a state sequence corresponding to each channel, wherein the speech activity state includes at least one of the following: initial silence state, speech state, intra-turn pause silence state, and inter-turn sentence segmentation silence state; and determining the time of sentence segmentation in the audio signal based on the state sequence. This application solves the technical problem of erroneous sentence segmentation in speech activity detection based on a fixed silence threshold in related technologies.

The invention provides an intelligent processing method and system from structured data to a text based on a natural language, and relates to the technical field of data processing.The method comprises the steps that the text chunk analysis process is optimized and adjusted according to analysis optimization parameters, sentences are segmented into non-overlapping phrases with syntactic function labels, and the text after chunk analysis is obtained; performing syntactic and semantic structure analysis on the text subjected to block analysis, and establishing a semantic association relationship between internal structures of sentences through component analysis, dependency analysis and semantic dependency graph analysis to obtain structured semantic information; performing multi-sentence logic association analysis on the structured semantic information on a chapter level to obtain semantic information of the whole chapter; and based on the semantic information of the overall chapter, obtaining a target natural language text by utilizing a pre-trained large language model. According to the method, the accuracy and fluency of conversion from the structured data to the text are improved.

The invention discloses an intelligent voice sentence segmentation method and system based on multi-modal fusion. The method comprises the following steps: acquiring voice data to be processed; processing the voice data by using a pre-trained acoustic sentence segmentation model to obtain an acoustic time sequence feature vector; processing the voice data by using a pre-trained text semantic model to obtain a semantic feature vector; and performing cross attention fusion on the acoustic time sequence feature vector and the semantic feature vector to obtain a fusion result, and determining a sentence segmentation result corresponding to the voice data according to the fusion result. According to the method and the device, the technical problem that the sentence segmentation position cannot be accurately determined due to the fact that acoustic features and semantic information are not fully combined in a related sentence segmentation method is solved.

The invention provides a text information recognition method and device, equipment and a storage medium. In some embodiments of the present disclosure, a first paragraph text and a second paragraph text of an audit report text are obtained; performing sentence segmentation processing on the first paragraph text and the second paragraph text to obtain a first sentence of the first paragraph text and a second sentence of the second paragraph text; combining the first clause and the second clause pairwise to obtain a sentence pair; encoding each sentence pair to obtain a sentence vector corresponding to each sentence pair; performing semantic emotion recognition on the sentence vector corresponding to each sentence pair to obtain a consistency result of each sentence pair; determining consistency information of the first paragraph text and the second paragraph text according to a consistency result of each sentence pair; based on semantic emotion recognition in the field of natural language processing, the audit report text is subjected to consistency auditing automatically, the labor cost is reduced, the auditing efficiency is improved, and the auditing accuracy is improved.

The application provides a role-aware chapter theme event argument extraction method and device, and the method comprises the following steps: obtaining argument role information of a chapter theme event of an event type according to the event type; performing sentence segmentation and title extraction on a target article to obtain a sentence set and an event title; the argument role information, the event type, and the event title constitute event-related information; constructing an argument role-aware graph by using the event-related information and the sentence set, performing event-related sentence detection, and obtaining a chapter theme event-related sentence set; taking the chapter theme event-related sentence set as input, constructing a question for each argument role, predicting all candidate arguments in the chapter theme event-related sentence set, and screening out a target argument from the candidate arguments. The method improves the model effect while maintaining the flexibility of the model.

The invention discloses a speech synthesis method and device, equipment and a storage medium, belongs to the technical field of speech synthesis, and is used for reducing the waiting time of a user in a speech synthesis process. The method comprises the following steps: acquiring a target text for speech synthesis, and segmenting the target text by adopting a plurality of preset short sentence segmentation modes to obtain a plurality of first short sentence character strings; based on the character length of each first short sentence, determining a first length and a first consumed time corresponding to each first short sentence through a preset corresponding relation table; based on the first length corresponding to each first short sentence, determining a first duration after each first short sentence generates voice, so as to determine waiting time of a user when streaming voice synthesis is carried out on each first short sentence character string according to the first duration and first consumed time corresponding to each first short sentence; and determining the first short sentence character string of which the waiting time is less than a preset threshold value as a target short sentence character string, and performing streaming speech synthesis based on the target short sentence character string.

The application discloses a multi-intention sentence segmentation method and device, a storage medium and an electronic device, relates to the technical field of smart homes, and comprises the following steps: extracting the character features of the characters included in a to-be-segmented sentence carrying multiple execution intentions; converting the to-be-segmented sentence into a feature image according to the character features, wherein the feature image is used to represent the character features through image pixels; performing semantic segmentation on the feature image to generate multiple semantic images, wherein each semantic image is used to represent a target execution intention in the multiple execution intentions; and identifying the target sub-sentence corresponding to each semantic image in the multiple semantic images to obtain multiple target sub-sentences corresponding to the to-be-segmented sentence. By adopting the technical scheme, the problems, such as low segmentation efficiency, in the process of segmenting multi-intention sentences in the related art are solved.

This application relates to a voice quality inspection method, device, computer device, and storage medium. The voice quality inspection method includes: converting voice data into text data, performing sentence segmentation processing on the text data to obtain text segments; configuring keyword types according to task types, retrieving keywords corresponding to the keyword types, and updating the keyword library; selecting keyword texts from the keyword library, comparing them with the text segments to obtain matching information of the keyword texts; comparing the matching information of the keyword texts with the number of selected keyword texts to obtain a matching coefficient of the keyword type; comparing the matching coefficient of the keyword type with a preset matching threshold to obtain a quality inspection result of the task type, which can avoid the defects of manual quality inspection methods, reduce enterprise costs, improve voice quality inspection efficiency, and realize the systematization of customer service performance evaluation and customer service satisfaction.

The present invention provides a method and system for processing local information context-related word components. The method inputs a sentence into a syntactic model, sets extraction windows of different widths for sentence segmentation, obtains a first word component, determines the length of the context content according to the extraction window, performs association calculations with the previous and next sentences respectively, determines the context content, performs semantic analysis on the first word component, and uses its context content as a reference input for semantic analysis to derive the word meaning corresponding to the first word component. By predicting the word meaning of the context content, the word meaning corresponding to the first word component can be corrected.

To solve the problem that it takes time to analyze contribution information and it is difficult to perform effective analysis.SOLUTION: An information processing system of the present disclosure includes acquisition means for acquiring post information posted for a predetermined store, classification means for acquiring a classification result obtained by classifying each divided sentence obtained by dividing a sentence included in the post information into a plurality of preset items according to content of the divided sentence, and output means for outputting aggregation information based on the number of classified divided sentences for each item based on the classification result, and outputting the divided sentence corresponding to the aggregation information. The information processing system according to (1).SELECTED DRAWING: Figure 2

The invention discloses a method, device and equipment for distinguishing identities in classroom dialogues and a storage medium. The method comprises the following steps: acquiring a classroom audio file; calling a preset voice recognition model to perform voice recognition on the classroom audio file to obtain text content in the classroom audio file; performing sentence segmentation on the text content by using a trained punctuation prediction model based on deep learning to obtain sentences of the voice text; calculating the voiceprint score of each sentence of the voice text; calculating the total visual score of each sentence of the voice text; and distinguishing whether each sentence of the voice text belongs to a teacher or a student based on the voiceprint score and the total visual score. According to the method, the identity in the classroom conversation can be distinguished simply, conveniently and effectively, the technical blank of a method for distinguishing the identity in the classroom conversation is filled, and a technical basis is provided for subsequent classroom analysis.

The present disclosure relates to a method, device, storage medium and electronic device for sentence segmentation. The method comprises: obtaining target audio data; extracting speech recognition text corresponding to the target audio data and a first time period corresponding to each recognized character in the speech recognition text in the target audio data; performing speaker segmentation on the target audio data to obtain a second time period corresponding to each speech segment in the target audio data; and performing speaker segmentation on the speech recognition text according to the first time period corresponding to each recognized character and the second time period corresponding to each speech segment to obtain a sentence segmentation result. Thus, the speaker time period information and the time period corresponding to each character in the speech recognition text in the target audio data can be effectively utilized to perform speaker segmentation on the speech recognition text, to reasonably and effectively segment the speaker conversion, to avoid a single sentence segment containing speech content of multiple speakers, and to improve the sentence segmentation effect.

The invention relates to the technical field of tourism product analysis, in particular to an elderly tourism product and experience behavior analysis management system based on big data. The user terminal obtains feedback content which is input by a user and aims at the tourism product, and then sends the feedback content to the server; the feedback content is processed to obtain text sentences corresponding to the feedback content, and the text sentences are tourism product judgment character evaluation content of the user and are subjected to sentence segmentation processing; the server carries out word recognition on the text sentences on the basis of a tourism item word bank and an emotion judgment word bank and generates an emotion score value corresponding to the feedback content, the emotion score value can reflect the evaluation degree of the user on the tourism product, the feedback content is displayed through the management terminal, and the user experience is improved. And according to the corresponding product numbers and the corresponding emotion score values, management personnel can perform targeted analysis and optimization on the tourism products according to the feedback content and the corresponding emotion score values.

The application provides a text recognition method and device, a storage medium and a vehicle. The method comprises the following steps: determining a first probability of a character in a to-be-recognized text in a preset annotation type according to a preset NLP model; determining a second probability of the character in the to-be-recognized text in the preset annotation type according to a preset CV network model and the first probability; outputting target annotation information according to the first probability and the second probability; and performing sentence segmentation recognition on the to-be-recognized text according to the target annotation information. The application determines the target annotation information of the to-be-recognized text to perform sentence segmentation on the to-be-recognized text, thereby improving the accuracy of sentence segmentation of the to-be-recognized text, improving the accuracy of text recognition, and facilitating accurate voice control according to the text recognition result.

The application relates to the technical field of natural language processing, in particular to an AI generated text detection method and system based on a deep learning model. The method comprises the following steps: performing character normalization and sentence segmentation on a to-be-detected text, segmenting the text by a sliding window to generate a segmented sequence; constructing deep semantic discrimination features, logarithmic probability difference trace features, style structure features and entity consistency features; inputting a gate fusion discrimination network, outputting a generated probability and confidence degree after probability calibration, and outputting a segmented contribution degree; performing threshold adaptive determination according to a risk level, a text length and the confidence degree, outputting a review state and generating a traceable record when the confidence degree is insufficient; and triggering incremental updating based on drift monitoring and a feedback sample pool. The application improves cross-domain robustness and interpretability, reduces false positives and false negatives, and supports long-term stable operation.

The invention discloses a multi-subject semantic attribute binding image generation method based on a diffusion model. The method comprises the steps of improving independent coding of a single sentence into segmentation of the sentence into a plurality of sub-sentences and independent coding of the sub-sentences; improving a denoising process and improving a cross attention mechanism; clear semantic representation is obtained by independently coding clauses, a denoising process is divided into a reconstruction branch and a generation branch, intermediate potential representation is spliced and fused to improve consistency and visual quality, and meanwhile, independent attention guidance is realized by combining an improved cross attention mechanism and mask division, so that feature aliasing is avoided, and the accuracy and the reliability of the system are improved. And the local precision and the space decoupling capability are enhanced. According to the multi-entity semantic modeling and image-text alignment method, a finer and more stable solution is provided while high efficiency and universality are ensured, and the potential in multi-entity semantic modeling and image-text alignment is shown.

The present invention relates to artificial intelligence and discloses a method for generating a text summary, including: performing sentence segmentation on historical reference articles to obtain a plurality of reference sentences; performing triple extraction and duplicate removal processing on the plurality of reference sentences to obtain a plurality of standard triples and performing label marking, using the data with label marking as a training data set, training a classification model using the training data set to obtain a standard classification model; inputting the article to be processed into the standard classification model to obtain a standard classification result; using the triples that meet the screening conditions in the standard classification result as target triples and performing triple splicing processing to obtain an input sequence, inputting the input sequence into a bidirectional long short-term memory network to obtain an article summary. In addition, the present invention also relates to blockchain technology, and the standard triples can be stored in the nodes of the blockchain. The present invention also proposes a text summary generating device, an electronic device, and a storage medium. The present invention can improve the accuracy of text summary generation.

The present invention provides an intelligent sentence segmentation method, a computer device and a storage medium based on a voice spectrogram. The method includes: obtaining voice data to be segmented, and converting the voice data to be segmented into spectrogram data to be segmented; identifying spectrogram silent segments according to the spectrogram data to be segmented; obtaining a pre-spectrum of a first preset duration before the spectrogram silent segment and a post-spectrum of a second preset duration after the spectrogram silent segment, and combining the pre-spectrum and the post-spectrum into a spectrogram to be recognized; using a preset classification model to recognize the spectrogram to be recognized, and confirming the pause category of the spectrogram silent segment; and performing sentence segmentation on the voice file according to the pause category. By applying the intelligent sentence segmentation method based on the voice spectrogram of the present invention, the accuracy of voice sentence segmentation can be effectively improved.

A kind of method for image retrieval based on NLP complex sentence segmentation, comprising: 1) complex sentence is segmented;2) multiple simple sentences are sorted;3) abstract sentence for retrieval image;4) query pruning;5) retrieve image;6) evaluate results: the performance of the method is evaluated using accuracy (Accuracy), precision (Precision), recall (Recall) and F1_score.The present application combines NLP with image retrieval, makes full use of the advantages of NLP, carries out detailed component syntax analysis and dependency syntax analysis on the complex sentence proposed by the user, inputs the sentence processed by analysis into the database as query sentence to retrieve image, not only greatly improves the effect of image retrieval, but also expands the application range of NLP.

The invention discloses an intelligent sentence segmentation activity voice detection method and device based on multi-state time sequence modeling. The method comprises the following steps: receiving an audio signal of at least one channel; extracting an acoustic feature sequence of the audio signal by adopting a target speech recognition model corresponding to the channel number; probability distribution that each voice frame corresponding to the acoustic feature sequence belongs to different voice activity states is determined, a state sequence corresponding to each channel is obtained, and the voice activity states comprise at least one of the following states: an initial mute state, a voice state, an in-speech-round pause mute state and a speech round discontinuous sentence mute state; and determining the sentence segmentation time in the audio signal according to the state sequence. According to the method and the device, the technical problem that wrong sentence segmentation exists in voice activity detection based on a fixed silence threshold in related technologies is solved.

The application relates to the technical field of natural language processing, and discloses a Chinese numeral quantifier correction method and system, computer equipment and a storage medium. The application constructs a dynamically expandable collocation word table and a numeral dictionary; performs sentence segmentation, word segmentation and part-of-speech tagging on input text, and identifies a three-element structure composed of numerals, quantifiers and nouns; excludes misjudgments on inherent expressions through a fixed phrase filtering mechanism; performs legality verification on the structure based on the collocation word table, and generates processing information for semantic correction if the verification fails; when collocation abnormalities are confirmed and the dictionary is insufficient, a large language model is triggered to replace the quantifier and adapt to the context based on the processing information, so that a final corrected sentence is generated. Through multi-layer cooperation of rule matching, word table verification and large model verification, the application effectively reduces the mis-correction rate while ensuring high-precision correction, and improves the accuracy and practicality of automatic correction of Chinese quantifiers.

The application provides a text summary extraction method based on joint training and a corresponding device, which are used to improve the problem of insufficient semantic correctness of the extracted summary text. The method comprises the following steps: obtaining a to-be-processed text, and performing sentence segmentation on the to-be-processed text to obtain a plurality of to-be-processed sentences; using a vector extraction layer in a summary extraction model to perform vectorization representation on the plurality of to-be-processed sentences to obtain word vectors and sentence vectors corresponding to the plurality of to-be-processed sentences; using a feature extraction layer in the summary extraction model to perform feature extraction on the word vectors and the sentence vectors corresponding to the plurality of to-be-processed sentences to obtain core feature vectors and similar feature vectors; and using a sentence extraction layer in the summary extraction model to extract the plurality of to-be-processed sentences according to the core feature vectors and the similar feature vectors to obtain a summary text corresponding to the to-be-processed text.